Sustainable urban transport planning

2012 ◽  
pp. 607-624 ◽  
Author(s):  
M. Grünig
Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 195
Author(s):  
Hua Chen ◽  
Ming Cai ◽  
Chen Xiong

With the rapid development of positioning techniques, a large amount of human travel trajectory data is collected. These datasets have become an effective data resource for obtaining urban traffic patterns. However, many traffic analyses are only based on a single dataset. It is difficult to determine whether a single-dataset-based result can meet the requirement of urban transport planning. In response to this problem, we attempted to obtain traffic patterns and population distributions from the perspective of multisource traffic data using license plate recognition (LPR) data and cellular signaling (CS) data. Based on the two kinds of datasets, identification methods of residents’ travel stay point are proposed. For LPR data, it was identified based on different vehicle speed thresholds at different times. For CS data, a spatiotemporal clustering algorithm based on time allocation was proposed to recognize it. We then used the correlation coefficient r and the significance test p-values to analyze the correlations between the CS and LPR data in terms of the population distribution and traffic patterns. We studied two real-world datasets from five working days of human mobility data and found that they were significantly correlated for the stay and move population distributions. Then, the analysis scale was refined to hour level. We also found that they still maintain a significant correlation. Finally, the origin–destination (OD) matrices between traffic analysis zones (TAZs) were obtained. Except for a few TAZs with poor correlations due to the fewer LPR records, the correlations of the other TAZs remained high. It showed that the population distribution and traffic patterns computed by the two datasets were fairly similar. Our research provides a method to improve the analysis of complex travel patterns and behaviors and provides opportunities for travel demand modeling and urban transport planning. The findings can also help decision-makers understand urban human mobility and can serve as a guide for urban management and transport planning.


2020 ◽  
Vol 12 (13) ◽  
pp. 5460 ◽  
Author(s):  
Tiziana Campisi ◽  
Nurten Akgün ◽  
Dario Ticali ◽  
Giovanni Tesoriere

The Sustainable Urban Mobility Planning (SUMP) process deals with barriers to improve accessibility and quality of life in urban mobility. Public opinion is highly essential for this process because it presents the real needs of road users. This paper illustrates the influence of public opinion on using Private Mobility Vehicle (PMV) in urban. A survey was carried out with 400 participants in Palermo, Italy. The results suggested that there was heterogeneity in gender and age groups in subcategories which represented people who use, do not use, and completely reject, using PMVs in urban. In addition, it was explored that there was a statistically significant relationship at 95% confidence level between sociodemographic characteristics (gender and age groups) and public opinion on PMV using. Employment status was found as an important parameter in transport planning. It was also showed that there was an inconsistency between local and national results. The results suggested that sociodemographic characteristics and public opinion should be investigated in further studies. In addition, a participatory planning process should be carried out to monitor for reliable evaluation in urban transport planning.


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